Swarm Intelligence to Face IoT Challenges

The Internet of Things (IoT) paradigm denotes billions of physical entities connected to Internet that allow the collecting and sharing of big amounts of data. Everything may become a component of the IoT thanks to advancements in hardware, software, and wireless network availability. Devices get an advanced level of digital intelligence that enables them to transmit real-time data without applying for human support. However, IoT also comes with its own set of unique challenges. Heavy network traffic is generated in the IoT environment for transmitting data. Reducing network traffic by determining the shortest route from the source to the aim decreases overall system response time and energy consumption costs. This translates into the need to define efficient routing algorithms. Many IoT devices are powered by batteries with limited lifetime, so in order to ensure remote, continuous, distributed, and decentralized control and self-organization of these devices, power-aware techniques are highly desirable. Another requirement is to manage huge amounts of dynamically changing data. This paper reviews a set of swarm intelligence (SI) algorithms applied to the main challenges introduced by the IoT. SI algorithms try to determine the best path for insects by modeling the hunting behavior of the agent community. These algorithms are suitable for IoT needs because of their flexibility, resilience, dissemination degree, and extension.


Introduction
In 1999, professor K. Ashton of the Massachusetts Institute of Technology (MIT) introduced the notion of the Internet of Tings (IoT). IoT is the combination of physical and digital objects embedded with sensors, actuators, network connectivity, and processing ability to produce new goods/ services and allow creative business strategies. It is now possible to digitize features and essential capabilities of industrial-era goods because of advancements in microprocessor technology, broadband connection, dependable memory, and power management [1]. As a result, a variety of opportunities are emerging for businesses to provide additional value in the IoT. IoT solutions are quickly being used in almost every aspect of daily life, and their range of applications is as broad as it is varied. Te most common application areas are in the felds of smart industry, smart applications, owing to the ongoing growth of smart environments [6].
Te goal is to simplify a complicated or more signifcant job by adopting an SI-based algorithm for achieving global optimization by modeling the IoT network as a collection of essential devices carrying out certain simple functions. Te objects cooperate utilizing decentralized control and selforganization in accordance with the SI tenets. A system becomes more efective, efcient, and scalable when a distributed type of control is used. SI is applicable to cloud computing scenarios, which makes it easier to solve multiobjective optimization issues. IoT data processing might become considerably quicker and more efective with its use. Once more, SI can address critical issues including cluster head selection, node localization, and routing protocol planning. IoT ecosystems would beneft from SI-based techniques in IoT-based systems, which would also enhance the user experience for IoT apps and services.
Te contribution of the paper is to summarize SI-based algorithms applied to the IoT for solving the main challenges of this technology. We have classifed a rich set of methods on the basis of the main SI algorithm employed, i.e., ant colony optimization (ACO), particle swarm optimization (PSO), and bee colony optimization (ABC), with respect to three main IoT concerns: (i) routing protocols and CH selection, (ii) power management, and (iii) data management.
Tis paper is structured as follows. Section 2 introduces the main SI algorithms. Section 3 presents the application of swarm intelligence algorithms to address IoT challenges. Section 4 discusses future directions and challenges before concluding the paper.

Swarm Intelligence Algorithms
Te study of large-scale, distributed systems that coordinate through decentralized control and self-organization, such as those seen in nature and in artifcial systems, is known as swarm intelligence. Tose who are looking for solutions get knowledge from other people's experiences. Collectively adjusting to the local and/or global environment maximizes a function or objective. Te idea of a swarm implies diversity, stochasticity, unpredictability, and disorder, but the idea of intelligence implies that the approach to problem solving is somehow efective. Te people that make up a swarm can be real or imagined, living, mechanical, computational, or mathematical, including (i) insects, birds, or humans and (ii) array elements, robots, or isolated workstations. Tere has to be an interaction between the persons for their connection to have a variety of features [7].
In [8] are defned the following SI basic principles: (i) Proximity principle: the general people should be capable of doing basic space and time calculations. (ii) Quality principle: the general people should react to environmental goodness variables. (iii) Diverse response principle: the general people should not conduct their afairs within too limited routes.
2 Computational Intelligence and Neuroscience (iv) Stability principle: the general populace should not alter its behavioral pattern in response to environmental changes. (v) Adaptability principle: individuals in the population must change their behavior when doing so is worthwhile from a computational standpoint.

Particle Swarm Optimization.
Including insects, cattle, birds, and fsh, the particle swarm optimization (PSO) technique aims to simulate colonial behavior in collective animals. Tese swarms adopt a cooperative approach to food gathering, and each swarm member continuously modifes the search pattern in response to its own and other members' growing experiences. Te early simple model of the algorithm, namely, the Boid model [10], is designed to simulate the behavior of birds. Based on this model, each bird has an initial velocity and position. According to the "next proximity velocity match rule," each person moves at the same speed as its closest neighbor. All of the points' velocities will eventually be equal if the iteration is continued in the same way. To replicate actual-world circumstances, each speed will be doubled by a random variable. Afterward, Russell and Kennedy [11] defned the particle swarm optimization (PSO) algorithm. Tey reduced each individual to a particle with no mass or volume, only velocity and location. Each entity in the swarm follows three major rules: (a) avoid collisions with nearby entities; (b) match its own velocity with the vicinity; and (c) fy to the fock's epicenter, and the swarm overall fies to the target. Te PSO algorithm is a swarm-based method of searching in which each component is referred to as a particle and is represented as a reasonable solution of the optimization problem in a D-dimensional examination space. It is capable of memorizing both the multitude's ideal position and its own, in addition to the velocity. Te particle data are combined in each iteration to change each dimension's velocity, which are then used to determine the particle's new position. Particles in the multi-dimensional search space continually alter their states until they attain a balance or ideal state or exceed the calculating boundaries. Te goal functions to introduce the unique relationship between several aspects of the issue space. A large body of empirical evidence has proven this method to be an efcient optimization technique.

Ant Colony Optimization.
Te ant colony optimization (ACO) technique is inspired by how ant colonies optimize their paths to food sources. One of the greatest illustrations of a natural swarm is an ant colony. Each ant sprays pheromones along the trail from their nest to the food source. After a while, the whole ant colony will be capable of taking the shortest route to the food because the ants in the colony will select the way with the highest pheromone intensity, and the ants moving along the path will create pheromones along the way. Solitary ants lack the intelligence to determine the quickest route to their meal. Nonetheless, they can readily complete a variety of difcult activities while living in colonies. ACO algorithms are used to solve optimization, traveling salesman, scheduling, and vehicle routing difculties, among other challenges. Strong global optimum capability and fexibility in deployment are two benefts of ACO. Te pheromone level is updated as the primary iteration in the ACO. It can be used in conjunction with other algorithms.

Bee Colony Optimization.
Te bee colony optimization (ABC) algorithm mimics a swarm of bees' honey-gathering activity. Another excellent example of a biological and dynamic swarm is a hive of bees. By distributing their labor among other bees, they demonstrate their intelligence. Bees exhibit various behaviors per their individual work divisions and understand the need for bee swarm communication and exchange of information to arrive at the best outcome. Tey carry out activities that include hunting, gathering, storing, distributing honey, gathering pollen, retrieving, and responding to changes. Te algorithm used to create the simulated bee colony separates the bee multitude into productive and jobless bees. When a food source is discovered among the latter, jobless bees scout it out. Worker bees randomly wander from one blossom to the next in search of a food supply. Te unemployed bees locate the closest route to the food source after it has been identifed, and when they do, they alert the other agents by doing a waggle dance. Employee bees gather nectar at the source and then take it back to their comb to store it. Each food source symbolizes a potential answer to the issue, and the quantity of nectar emanating from the source suggests the potency of the answer. Te function of the bees may be switched around in the artifcial colony of bees approach, Computational Intelligence and Neuroscience which makes it diferent from prior SI algorithms. For instance, it should be stopped if the source data do not change after multiple rounds. ABC's benefts include its excellent global search capability and rapid convergence speed. Its downsides include a lack of racial variety. It is possible for the solution to enter the local optimum when it is close to the global optimum, resulting in a situation of stagnation [6].

IoT Challenges Faced by SI
Te Internet of Tings has transformed the traditional way of life into a modern one. Smart cities [12][13][14], smart homes [15,16], smart health [17][18][19], smart industries [20], smart transportation [21,22], agriculture [23,24], energy saving [25], and pollution control [26] are such transformations due to IoT. Several important research projects and studies have been carried out in an efort to advance technology through IoT. To fully realize the promise of IoT, several obstacles and problems must be resolved. Standard communication protocols must be adopted for efective device integration, yet IoT devices do not adhere to the same standards and protocols. Because of this, the nonuniformity of their qualities makes data aggregation more challenging, slowing down the entire process and reducing IoTscalability. Te volume of recorded data is growing as a result of linked device proliferation, high-resolution instrumentation, and big populations. Computer scientists face enormous hurdles due to the surge in data generation, including data storage, access, visualization, analysis, and modeling issues. Although we can typically reduce and resolve issues like scalability, resource constraints, and data management using cloud computing, parallel computing, and artifcial intelligence, more work still needs to be done.
Cloud computing and IoT are separate yet robust systems integrated to become essential to the Internet's future. Te idea behind this integration is that IoT devices are networked sensors designed to collect data and transmit it to the cloud for computing. An important requirement is to provide reliability and low-latency analysis ofoading, enabling forthcoming IoT services with stringent requirements [27]. Because of the considerable amount of confdential data stored in the cloud, data protection and privacy are major unresolved concerns [28].
Te majority of smart devices lack information standards and protocols, and the laws governing data ownership are unclear. Due to all of these variables, the data are quite vulnerable to hackers who can compromise personal information by breaking into the system. In addition to other things, the power management of smart devices is a significant problem because of the limited capabilities of the devices and the high application needs. Most IoT devices are battery-powered, which provides essential benefts in terms of mobility, freedom of installation, fexibility, and ease of use; however, the harder an IoT device has to work, the more power it will draw.
Swarm intelligence is an excellent ally to deal with some of the problems that IoT technology's maturation brings. For instance, a model called DF-IDS is proposed in [29] and is modeled on spider monkeys' foraging behavior. Via two primary steps, it seeks to fnd IoT trafc intrusions. In the frst step, it competitively chooses the most suitable components from the characteristic matrix utilizing Spider-Monkey (SM), principal information gain (IG), component analysis (PCA), and correlation attribute assessment (CAE). Tese attributes are employed in the second step to train a deep-NN for intrusion detection. In [30], the authors applied a distributed hashing model to perform the goodness of large amounts of data collected by devices, bearing in mind their processing and power capabilities. Tis section ofers an overview of SI algorithms applied to three main IoT challenges: (i) routing protocol planning and cluster head selection, (ii) power management, and (iii) data management.

Routing Protocols and CH Selection Methods.
A considerable percentage of SI-based routing protocols have emerged in wireless sensor networks (WSNs). Increasing performance, preserving energy, and ensuring secure communication are the main goals of WSN design-all essential components of a reliable IoT system. Te WSN-IoT provides opportunities and difculties. Many SI-based techniques have been created in accordance with the types of WSN-related challenges. Due to the extremely huge scale of WSN, where nodes are frequently installed at random, it is necessary to take into account the efective design of communication protocols in such networks. Protocols must be resilient to errors and losses as well as selforganizing. Moreover, a well-planned routing protocol allows saving energy, which is a key requirement in WSN.
Routing protocols in WSNs may be divided into fat protocols and vertical routing methods based on communication logic [31]. Networks are divided into clusters of varying sizes in hierarchical routing protocols, and cluster head (CH) identifcation is a critical topic. Each cluster is made up of a CH and many cluster components. Te representative of the cluster oversees or controls all of the cluster's nodes, coordinates activity among member nodes, and is in charge of information collecting in the cluster, data fusion processing, and cluster forwarding. As a result, CH energy consumption is often higher. Te WSN relies on optimum routing methods to preserve communication energy and hence extend the network lifespan.
A broad overview of algorithms that concentrate on this problem is presented in [6,9]. Based on these references, we have classifed routing protocols and CH selection methods on the basis of the SI algorithm employed, as described in Table 1. [32] proposed a PSO and Tabu search-based WSN routing optimization method. With the aim of increasing the network lifetime, the Tabu-PSO method tries to maximize the number of clusters and nodes in each cluster. Te CH in the cluster that uses the least energy is also picked. Te fndings show that increasing the amount of clusters and boosting node survivability can dramatically lower average end-to-end latency and mean packet loss rate. 4 Computational Intelligence and Neuroscience

PSO-Based Algorithm. Te authors in
Reference [33] presented the EMSP routing method, which incorporated artifcial clustering approaches and mobile source technologies in the routing management. For selecting CH, the algorithm considers residual energy and node position (distance node -center of gravity).
Te mobile sink selects the node with leftover energy that is more than the mean residual energy of all the cluster's networks as a new CH after examining all cluster heads. Te group with the most energy remaining is where the node initiates collecting data from. Te member node will not transmit messages to the CH until the sink has determined which CH it is. In this case, it is very likely that the member node will fail frst before any data are sent. According to the data, both the average delivery delay and network life have improved.
Another PSO-based CH selection strategy and a cluster creation based on weight operation are proposed [34]. In addition to the commonly used distance from non-CH to CH, the cluster formation technique also takes into account the distance from the access point to CH, the CH's distance, and the CH's remaining energy. Te technique takes into account WSN capacity balancing and fault tolerance and only operates on isomorphic networks. Following these concepts, a method based on fuzzy clustering and PSO was proposed in [35]. Te overall number of disconnected sensors for all clusters is what the PSO algorithm uses as its basis for trying to fnd CH. In order to minimize network interruption, the best CH is then used to update the fuzzy C-means (FCM) algorithm. In order to determine the optimal sensor topology, the hybrid FCM-PSO approach is applied. Te quantity of sensors that cannot be linked to the CH and the proportion of CHs that cannot contact the station in 3D-WSN can both be decreased, according to simulation results, using this technique.
In [36], a PSO-based clustering technique (EC-PSO) in a heterogeneous environment is described. Te network's energy center is located using the PSO, and the nodes that are closest to it are selected as CHs. Te recommended technique states that CH should be located in the energy center since CH and the nodes around it use a lot of energy. Energy holes are prevented from forming through clustering. A low-energy protection mechanism was created, and a mobile data collector was set up to collect data in order to prevent the weak node from turning into a relay node. Te technique performs well in terms of energy usage.
An optimization method based on the improved PSO algorithm was applied in [37] to extend the target nodes throughout the network life cycle. Te protocol accounts for both transmission distance and energy efciency, and relay nodes are employed to reduce the cluster heads' excessive power consumption. Using the suggested protocol, the network's lifespan is increased through more evenly distributed sensors and a balanced clustering scheme.
In [38], in order to build, recover from, and choose kdisjoint paths that allow failure while preserving the quality of service features, authors provided a bio-inspired PSObased routing method. By exchanging messages from all locations in the network, the approach enables identifying the optimum routes while using multi-path routing. Compared to the standard PSO, the overall energy consumption and latency are decreased. Because the method takes a while to stabilize the feasible solution in the start of the iteration, there is a little disadvantage.
When a path fails in the Internet of Tings, the authors in [39] propose an enhanced, efective, and adaptive faulttolerance routing algorithm (IEIFTA) to ofer quick routing recovery and reconstruct the network architecture. With the use of a multi-swarm evolution equation, the IEIFTA, a PSO-based method, determines the direction in which the particle will mutate. Te immune mechanism boosts the efciency of global search and the algorithm's rate of convergence.  [40]. Te algorithm uses the modifed ACO to choose the most efective sink movement path, communicate with all CHs, and collect information via a single-hop closer contact. Te heuristic factor is maximized to achieve this (visibility). Tis method, which only rotates CH whenever a CH remaining energy does not exceed the energy threshold, can increase the network life compared to earlier ACO-based standard algorithms. A cross-layer protocol (FAMACROW) was proposed in [41]. Fuzzy logic is employed in this method to choose CH, and ACO technology is utilized to perform multi-hop routing from a cluster to the master station. Te approach employs connection quality indicator, neighborhood proximity, and residual energy as input parameters. Using connection quality metrics to choose CH and transmit the likelihood of routes between clusters increases the dependability of protocols. In terms of latency, throughput, and network life, FAMACROW outperforms the other uneven clustering routing.
In [43], the authors proposed an ACO-based routing algorithm in order to determine the WSNs' ideal route for data transmission. Te algorithm uses an improved heuristic function and considers location information and search direction. In particular, it takes into account both the distance between one node and its sink as well as the distance between nodes. When the sink and the objective region are far apart, the method performs noticeably better than previous algorithms.
Another routing protocol, named SRPMA and based on ACO, is proposed in [44]. Tis method seeks to improve multi-path routing outcomes. Te primary idea is to incorporate the Pareto multi-objective approach with the ACO. As optimization targets, the lifetime of devices and the reliability of a routing pathway are considered. Te node trust management model is developed by evaluating the node's trust level using an upgraded D-S evidence model with preprocessing. In WSN routing, the SRPMA can achieve performance requirements against the black hole attack when compared to similar ACO-based security routing protocols.
In [45], ACO was combined with a label propagation (LP) algorithm. CB-RACO is the proposed routing protocol's name. High data transmission speeds are desired for large-scale WSNs. Even though this protocol divides clusters and is not CH-based, each ant can only reside in its own cluster (cluster activity). Te beneft of this method is that it can manage and operate the network independently when it is overloaded and at a very cheap memory cost.
Te authors of [46] presented an ACO-based routing mechanism to improve packet delivery speed and prevent overlapped junctions by utilizing multi-agent technology. According to the network type, the proposed method separates the IoT ecosystem into diferent sections. Te ACO algorithm that is most suited to each network is then chosen.
A dual entity is created to generate an optimum algorithm from various ACO methods.
Similarly, the IoT environment in [47] is separated into segmented zones based on network types. Te most appropriate ant colony algorithm is then applied to the relevant network inside each location. Each network is assigned an ACO in charge of controlling the routing process. Te authors suggested a technique for regulating the employment of ACO algorithms and fguring out a solution for covered regions. Te routing algorithm's goal is to optimize the choice of the most suitable path for data transmission inside the IoT system.
In [48], ACO was integrated with RPL-IPv6 routing protocol. RPL ofers a 6LoWPAN abstraction layer routing solution that uses the least amount of energy possible for the sensors. In order to maximize path power usage from overexpected transmission cost metric-based cost function, which represents the sum of energy consumption by the transmitter during communication and the listening base station during listening, the proposed approach ofers an approach to switch from the failing connection to another efective link.
A hybrid multi-path routing algorithm was proposed in [42]. Te approach is based on the joint use of ACO and ABC and is called exponential ant colony optimization (EACO). Te fractional ABC (FABC) method is used in the frst phase to fnd CHs with a ftness function that takes distance, power, and delay into account. Te ACO method is altered with an exponential smoothing model supporting multi-path route fnding in the second phase. In order to move data from any source address to the gateway destination with the least amount of energy, location, and intra/ inter-cluster delay, EACO determines the best pathways among CHs. Tese goals are well-stated as new ftness metrics to choose the best course of action.
In [49], the optimal cluster head was selected from a group of nodes using the butterfy optimization algorithm (BOA). Te distance to neighbors, the distance to the access point, the node degree, and the node centrality all have an impact on the cluster selection. ACO is exploited to determine the path between the cluster and the base node. It chooses the best path based on the distance, remaining energy, and node degree. Te energy use, data packets obtained by the BS, living nodes, dead nodes, and other performance metrics of this suggested approach are examined. Te study's goals are to reduce total energy usage and increase network longevity.

ABC-Based Algorithm.
A centralized clustering-based routing protocol exploiting the ABC to adjust fuzzy rules was proposed in [50]. Te outcomes demonstrate that the LEACH-SF algorithm is capable of minimizing distances inside clusters while maximizing network lifespan and base station packet reception rates. Moreover, it possesses the ability to be enhanced for multi-hop routing and mobile sensor nodes. On a heterogeneous network, the simulation is built.
In [51], the authors suggested a method for choosing the most signifcant cluster heads. Tey empower the ABC algorithm with the gravitational search algorithm (GSA). Until it achieves the halting condition, GSA updates the agents' positions and speeds. In this situation, the ABC algorithm's updated employed bee phase is updated using the GSA algorithm. During the selection of cluster heads, the IoT devices' distance, energy, latency, load, and temperature are taken into account.
Tis section collects numerous SI-based algorithms to address the problems of routing and CH selection in IoT environments. Tese algorithms explicitly or implicitly aim at decreasing network energy consumption, which is one of the main problems for such systems. Te next section focuses on algorithms that ofer power management techniques in IoT.

Power Management Methods. Te energy management of mobile devices, sensors, wearable technology, and other
IoT items is crucial in IoT contexts. As IoT devices run on batteries and energy conservation has emerged as one of the industry's top issues, this section examines several strategies centered on that issue. We discussed diferent energyefcient and SI-based techniques proposed in state of the art. Most of the following references focused on energyconstrained IoT-based WSNs. An active study area in IoTbased systems is extending the network's lifetime while attaining optimal coverage WSNs. It is much more challenging than any other kind of network.
In Table 2, power management methods are classifed on the basis of the SI algorithm employed.

PSO-Based Algorithms.
Energy-efcient communication is the goal of IoT. Bluetooth Low Energy is an intriguing option for wireless communications. Te Bluetooth 5 standards were recently released with the intention of providing notable improvements over the protocol's prior iterations. In [52], to deal with choosing the Bluetooth 5 connection mode that provides for the highest energy effciency, the authors proposed a fuzzy-based method. Te idea is to adjust a fuzzy logic controller's transmission power output in order to dynamically control the communication mode utilized by mobile devices (FLC). To determine the optimal performance conditions values for the suggested FLC, a PSO method is described. In particular, PSO enhances triangular membership characteristics by modifying their range to obtain the best results for mobile device battery capacity. Triangular membership functions are considered the foundation of the proposed FLC because they ofer a reasonable balance between computational cost and efectiveness.
Energy harvesting techniques are signifcant IoT power management research areas. Te energy-collecting device may directly tap into the immediate environment, including pressure, vibration, wind, temperature gradients, and the sun's energy source. In [53], the authors have demonstrated a brand-new, enhanced power management unit (PMU) that is suitable for IoT applications and may be included in an energy collection system. Solar cells, turbines, thermal, and vibration may all be included in the system. Trough the use of a buck-boost DC-DC converter, the optimized PMU is created. Te PMU's characteristics, with the converter and control circuits, are then optimized using the PSO method to increase overall power efciency and reach maximum power point. Te primary goal of using PSO is to choose appropriate inductor and MOSFET on-time values while computing and simulating power loss equations to reduce the energy consumption of the inductor, photodiode, MOSFET, and management system.
In [54], a PSO-enhanced IOT-based power point tracking (MPPT) method was suggested for solar photovoltaic (PV) systems. Te most promising renewable energy source is the photovoltaic system, and tracking of maximum power points (MPPT) is crucial to the smooth operation of any PV-based powered system. Smart meters, sensors, and actuators are all incorporated into IoT-based technologies to enable MPPT operation of the photovoltaic electricity system. Tey enable distributed connection and automation for management and oversight of the MPPT system. Te antenna receives processed sensor data. Moreover, IoT helps dynamically identify the MPP region of solar modules. In this study, a modifed DC-DC ZETA converter is an interface between the DC load and the solar PV. Te PSO-IoT method uses Arduino and Bluetooth to continually modify the converter's duty cycle in order to gather the most power possible. Te suggested PSO-IOT MPPTapproach has a high PV tracking efciency and, in comparison to other optimization techniques mentioned in the literature, converges quickly with little variations around Global MPP (GMPP).
Hurtado et al. [55] proposed an agent-based strategy to improve how the SG-BEMS framework interacts with one another. It is necessary to go from a "vertical" to a "horizontal" form for future power systems. As buildings account for a large portion of total energy consumption, it is crucial to manage the futuristic power grid and built environment in a way that promotes energy efciency and environmental responsibility. A sophisticated building energy control system (BEMS) is needed to handle the extremely complicated interactions between two environments throughout this evolution to a smart grid. A PSO optimizer is suggested in order to increase multi-agent systems' (MAS) capacity for taking use of the building's adaptability for the smart grid. PSO is employed to increase comfort and energy ABC Muhammad et al. [58] Yousef et al. [59] Vijayashree and Dhas [60] Mann and Singh [61] Computational Intelligence and Neuroscience efectiveness. According to a numerical result from an integrated simulation, the building's operation may be dynamically changed to help the local power grid's voltage management without impacting the building's principal function, namely, comfort provision.

ACO-Based Algorithms.
With the help of a brand-new ACO algorithm called three-pheromone ACO, Lee et al. [56] provided a new method to address the efcient-energy coverage issue in WSN (TPACO). In contrast to existing ACO algorithms, which only use one form of pheromone, the proposed ACO algorithm uses three diferent kinds of pheromones to identify the solution successfully. In contrast to the other two pheromones, which are used to form a collection of sensors that contains as many sensors as the ant has selected the number of active sensors to be employed, the local pheromone assists the ant in organizing its coverage set so that it employs fewer sensors. Te TPACO algorithm also has the advantage of not requiring the two separate user variables of the ACO algorithm. Te authors also presented two methods that help address the efcient-energy coverage challenge in a more practical way. Te probability-based sensor detection model is used as the frst method. Te second technique involves using many types of sensors, or heterogeneous sensors, in continuous space rather than a discrete region based on a grid. Results from the simulation show that the recommended technique is efective in terms of network longevity.
With the same aim, in [57], in order to discover the best route for recruiting sensor nodes for signal transmission, a customized version of the ACO algorithm is used. Te intra-node spatial range and the speed of battery fall out/ recovery with regard to signal transmission are combined in the suggested technique.

ABC-Based Algorithms.
In [58], for fnding the ideal number of disjoint subsets, the HABCA-EST method, a hybrid ABC algorithm including an efcient schedule transformation, was presented. Te objective is to extend a wireless smart device network's life for applications that require target coverage. Nondeterministic polynomial completeness characterizes this issue. Te distinguishing characteristic of HABCA-EST is the explosive development of the ftness function as a result of the full exploitation of surplus information among the planned devices. In HABCA-EST, the swarm and EST processes cooperate to fnd an ideal solution in less time efciently.
In [59], an energy-efcient clustering mechanism was developed. It consists of two primary stages. Te ABC method is used in the frst step to choose the nearly ideal cluster heads. Performance requirements for a device include its remaining energy, the quantity of nearby devices, the Euclidean distance (ED) between it and the source, and the ED from it to each of its neighbors. Te principal objective of the second phase is to cluster devices into a small number of groups based on the amount of data that each group produces, as well as the ED between each group's leader and members. It has been proven that the authors' approach is efective in terms of energy consumption, durability, and transmission delay.
Te same objective drove the work done in [60,61]. Distributed mobile sink-based method for collecting data, which combines energy-balanced clustering with ABCbased data gathering, was proposed by the authors in [60]. Te cluster head is determined by the node's remaining energy. Tey looked at mobile sink balancing from three angles: maximizing data gathering, minimizing mobile route length, and improving network dependability. Simulation fndings demonstrate that, in contrast to existing algorithms like random walk and ACO, the recommended approach may efectively reduce data transfer, save energy, improve the accuracy and dependability of network data collection, and enhance network lifetime.
To strengthen the suggested metaheuristic's worldwide convergence and to improve its exploitation capabilities, in [61], an enhanced ABC (iABC) evolutionary algorithm with a better search equation was designed. Student's Tdistribution is used to create signifcant sampling techniques since it only needs one regularization term to be computed and stored. To create the best cluster heads and boost WSN energy efciency, the iABC optimization algorithm is also employed to develop a power bee clustering protocol. Tis protocol inherits the efcacy of the recommended metaheuristic. According to the fndings of the simulation, the suggested strategy performs well in terms of availability, packet delivery, use of energy, and network durability.

Data Management
Methods. Te volume of recorded data is growing as a consequence of the expansion of IoTlinked infrastructures, high-resolution equipment, and big populations. Computer scientists face enormous hurdles as a result of the surge in data generation, including issues with data storage, access, presentation, analysis, and modeling.
Data management and mining technologies are fundamental in various IoTapplications; as a result, it is possible to develop more robust models, and swarm technologies can assist with this. In Table 3, we have classifed data management methods on the basis of the SI algorithm employed.

PSO-Based Algorithms.
Te use of data mining and intelligent systems for IoT was discussed in [15]. Each sensor is connected to a straightforward agent in the authors' sophisticated data management platform founded on swarm optimization. ACO and PSO have an impact on how each agent senses. By sharing information and experience among themselves, agents act like a swarm. To further explain the fundamental ideas of DMFSO, the authors utilized a straightforward example of a smart house to demonstrate information sharing and decision making.
A PSO-based backpropagation (BP) artifcial neural technique to large data mining is suggested by Zhou et al. in [62] for economic risk management in fnancial institutions with a smart implementation. Using the collection of on/ of-balance sheet items, it constructs a nonlinear parallel optimization approach using Apache Spark and Hadoop To efectively increase the measurement precision of physiological multi-sensor data fusion in IOT, an enhanced PSO approach (IPSO) is presented in [63]. IPSO incorporates shrinkage factor adjustment and inertia weight factor design. Te multi-physiological data processing and real-time medical care of things analyses are built on the Android platform. [65] proposed using ACO for data optimization to shorten the time required for processing all of the data. For quick data mining on larger datasets, ACO provides large-scale optimization. It can process high-dimensional data to ensure that the performance of the algorithm is not adversely afected by massive datasets, and it can handle dynamic data, enabling practically real-time data processing. It supports multiobjective optimization in ACO, which enables the management of data from various sources. Tis is a reliable and efective method for IoT data optimization.

ACO-Based Algorithms. Te authors in
In [64], an ACO-based clustering method (ACOCA) is suggested for massive data preprocessing. Te hybrid algorithm can facilitate faster search by streamlining the procedure. Te combined use of ACO with the clustering method further speeds up preprocessing and improves the precision and efectiveness of analysis.
In order to increase the exploitation of agricultural big data and address the security concerns associated with multi-source and heterogeneous agricultural big data, the authors in [24] suggested an enhanced agricultural big data ACO algorithm (BigDataACO). Te approach tackles the issue of multi-source data fusion and ofers excellent resilience and parallelism potential. Findings demonstrate that the improved method described in this research signifcantly reduces the uncertainty of data fusion when compared to D-S evidence theory, K-means, and Bayesian algorithm.
In [13], in order to automate data processing and provide useful information to be utilized in the autonomous management of trafc circulation in major metropolises, the authors addressed the issue of collecting data from many sensors. Tey presented a brand-new platform created for the intelligent administration of automated environments by heavyweight agents and the integration of diverse sensors utilizing lightweight agents. Tese later ones have information fusion expertise. One of the ACO models, H-ABC (hierarchical ant-based control), is used by specialized agents to carry out the required data transformations.

ABC-Based
Algorithms. Due to its advantageous characteristics, such as having few controllable parameters, being very fexible, and having a powerful global search capability, ABC has been exploited to solve several optimization issues [66]. Large datasets are processed and features are chosen using it as well. A Hadoop-based ABC (H-ABC) strategy for feature extraction in medical big data IoT was presented in [19]. ABC is used with traditional Map-Reduce to enhance processing efciency. A parallel technique that supports MapReduce efectively handles a huge number of datasets. Using 10 distinct datasets, the suggested method is compared to swarm techniques and assessed in terms of efectiveness, accuracy, and throughput. Te fndings demonstrate how the suggested approach is more scalable and efective in choosing characteristics.

Challenges and Future Directions
IoT is a paradigm that demands energy savings and real-time management or a scenario with a quick reaction and high transmission rate. IoT settings are intricate, sizable, and distributed systems that face difculties with heterogeneity, security, and dependability. Tey must also consider a number of challenges related to efciency, scalability, security, real-time reactions, and smartness. Tis results in the requirement for exceedingly sophisticated system design and construction. Due to its resilient, scalable, and selforganized characteristics, swarm intelligence is a proftable paradigm for dealing with complex, rapidly changing, and dynamic situations. In this survey, swarm intelligence-based techniques applied to IoT have been analyzed. To face vulnerabilities present in IoT environments, social creatures' behavior has been a matter of study for the development of optimization algorithms. We focused on the most popular swarm intelligence algorithms: the classical particle swarm optimization (PSO) and the artifcial bee colony (ABC). We included in the present work a wide range of papers that proposed solutions to IoT challenges based on the principles of the aforementioned algorithms. Concerning the IoT challenges, we address three main problems that can undermine the performance of these technologies, with a focus on optimizing network trafc, energy, and data.
As future works, we propose to extend the paper considering works based on a broader set of swarm intelligence algorithms and their application to solve both discussed and new IoT challenges.
To provide only a few examples, the authors in [67] proposed a hybrid intelligent optimization algorithm (HIOA) to reduce total energy usage in an IoT network. Fuzzy logic, a genetic algorithm, and the chicken swarm optimization (CSO) method are all used by HIOA to build  [63] the best possible clusters. Te unbalanced CH distribution is addressed by the CSO, which also proves to be a fair network load.
An energy-efcient cloud-based Internet of Everything (EECloudIoE) framework is suggested in [68] with the goal of extending the lifespan and decreasing trafc of IoT environment. In this study, the multiple IoT networks are clustered using the wind driven optimization technique to maximize energy usage. Te next step is to select an optimal cluster head (CH) for all clusters employing the frefy algorithm to help reduce the amount of trafc that will be stored in the cloud.
Te integration of swarm intelligence with IoT opens up opportunities for solving complex problems in a decentralized, scalable, and efcient manner. Te future direction of swarm intelligence in IoT is promising, with a focus on improving the following: (i) Scalability: IoT devices have limited computational and storage capabilities, which can make it challenging to implement swarm algorithms that are scalable for large networks of devices. (ii) Real-time communication: swarm algorithms require efcient communication between devices, which can be difcult to achieve in real time in large-scale IoT networks. (iii) Energy efciency: IoT devices have limited energy resources, so the swarm algorithms must be energyefcient in order to preserve battery life. (iv) Privacy: swarm algorithms often collect and exchange data among devices, which can raise privacy concerns. (v) Robustness: swarm algorithms need to be robust against network failures, faulty devices, and malicious attacks, which can be difcult to achieve in a distributed IoT environment. (vi) Integration with other technologies: swarm algorithms must be integrated with other advanced technologies, such as artifcial intelligence and edge computing, to fully leverage their potential in IoT applications.
Despite these challenges, the use of these algorithms has a lot of potential for future development, such as in the areas of smart cities, smart homes, and autonomous systems.

Conclusion
Te world around us is becoming more intelligent and more responsive thanks to the Internet of Tings (IoT), which combines the digital and physical worlds. IoTmay be applied in countless felds and has an impact on how we live our daily lives. Te widespread use of mobile devices and Wi-Fi has made this technology possible. Te network of IoT devices ofers many undeniable benefts, like direct communication between separate devices without human interference, easy access to wide-scale and real-time data, and remote and continuous device control. Even though they are potential advantages, these new possibilities also present some challenges. Many obstacles are being imposed across multiple dimensions by the rapid growth of IoT-connected devices, including the limited lifespan of battery-operated devices, uneven load distribution, high transmission latency, and data security, complexity, and volume.
SI is a collection of nature-inspired searching techniques. Several animals in nature, including birds, fshes, and bees, exhibit group behaviors. Te collective talents of a group are far more lively than the individual skills of its members. A simulation technique to model biological group intelligence is the SI algorithm. Several large-scale, dynamic, and multiobjective issues have been successfully handled via SI. In general, all SI-based metaheuristics have demonstrated benefcial adaptive features for solving optimization issues afecting IoT.
Tis work reviewed swarm intelligence (SI) algorithms to tackle three main IoT challenges: (i) routing protocols and CH selection, (ii) power management, and (iii) data management. Forthcoming research proposes to summarize papers that address other IoT issues considering a wider set of SI algorithms beyond those already considered in this work.

Data Availability
No data were used to support this study.

Conflicts of Interest
Te authors declare that they have no conficts of interest.